# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math

import torch

# Value for clamping large dw and dh predictions. The heuristic is that we clamp
# such that dw and dh are no larger than what would transform a 16px box into a
# 1000px box (based on a small anchor, 16px, and a typical image size, 1000px).
_DEFAULT_SCALE_CLAMP = math.log(1000.0 / 16)


__all__ = ["Box2BoxTransform", "Box2BoxTransformRotated"]


class Box2BoxTransform(object):
    """
    The box-to-box transform defined in R-CNN. The transformation is parameterized
    by 4 deltas: (dx, dy, dw, dh). The transformation scales the box's width and height
    by exp(dw), exp(dh) and shifts a box's center by the offset (dx * width, dy * height).
    """

    def __init__(self, weights, scale_clamp=_DEFAULT_SCALE_CLAMP):
        """
        Args:
            weights (4-element tuple): Scaling factors that are applied to the
                (dx, dy, dw, dh) deltas. In Fast R-CNN, these were originally set
                such that the deltas have unit variance; now they are treated as
                hyperparameters of the system.
            scale_clamp (float): When predicting deltas, the predicted box scaling
                factors (dw and dh) are clamped such that they are <= scale_clamp.
        """
        self.weights = weights
        self.scale_clamp = scale_clamp

    def get_deltas(self, src_boxes, target_boxes):
        """
        Get box regression transformation deltas (dx, dy, dw, dh) that can be used
        to transform the `src_boxes` into the `target_boxes`. That is, the relation
        ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless
        any delta is too large and is clamped).

        Args:
            src_boxes (Tensor): source boxes, e.g., object proposals
            target_boxes (Tensor): target of the transformation, e.g., ground-truth
                boxes.
        """
        assert isinstance(src_boxes, torch.Tensor), type(src_boxes)
        assert isinstance(target_boxes, torch.Tensor), type(target_boxes)

        src_widths = src_boxes[..., 2] - src_boxes[..., 0]
        src_heights = src_boxes[..., 3] - src_boxes[..., 1]
        src_ctr_x = src_boxes[..., 0] + 0.5 * src_widths
        src_ctr_y = src_boxes[..., 1] + 0.5 * src_heights

        target_widths = target_boxes[..., 2] - target_boxes[..., 0]
        target_heights = target_boxes[..., 3] - target_boxes[..., 1]
        target_ctr_x = target_boxes[..., 0] + 0.5 * target_widths
        target_ctr_y = target_boxes[..., 1] + 0.5 * target_heights

        wx, wy, ww, wh = self.weights
        dx = wx * (target_ctr_x - src_ctr_x) / src_widths
        dy = wy * (target_ctr_y - src_ctr_y) / src_heights
        dw = ww * torch.log(target_widths / src_widths)
        dh = wh * torch.log(target_heights / src_heights)

        deltas = torch.stack((dx, dy, dw, dh), dim=-1)
        assert (src_widths > 0).all().item(), "Input boxes to Box2BoxTransform are not valid!"
        return deltas

    def apply_deltas(self, deltas, boxes):
        """
        Apply transformation `deltas` (dx, dy, dw, dh) to `boxes`.

        Args:
            deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1.
                deltas[i] represents k potentially different class-specific
                box transformations for the single box boxes[i].
            boxes (Tensor): boxes to transform, of shape (N, 4)
        """
        assert torch.isfinite(deltas).all().item(), "Box regression deltas become infinite or NaN!"
        boxes = boxes.to(deltas.dtype)

        widths = boxes[..., 2] - boxes[..., 0]
        heights = boxes[..., 3] - boxes[..., 1]
        ctr_x = boxes[..., 0] + 0.5 * widths
        ctr_y = boxes[..., 1] + 0.5 * heights

        wx, wy, ww, wh = self.weights
        dx = deltas[..., 0::4] / wx
        dy = deltas[..., 1::4] / wy
        dw = deltas[..., 2::4] / ww
        dh = deltas[..., 3::4] / wh

        # Prevent sending too large values into torch.exp()
        dw = torch.clamp(dw, max=self.scale_clamp)
        dh = torch.clamp(dh, max=self.scale_clamp)

        pred_ctr_x = dx * widths[..., None] + ctr_x[..., None]
        pred_ctr_y = dy * heights[..., None] + ctr_y[..., None]
        pred_w = torch.exp(dw) * widths[..., None]
        pred_h = torch.exp(dh) * heights[..., None]

        pred_boxes = torch.zeros_like(deltas)
        pred_boxes[..., 0::4] = pred_ctr_x - 0.5 * pred_w  # x1
        pred_boxes[..., 1::4] = pred_ctr_y - 0.5 * pred_h  # y1
        pred_boxes[..., 2::4] = pred_ctr_x + 0.5 * pred_w  # x2
        pred_boxes[..., 3::4] = pred_ctr_y + 0.5 * pred_h  # y2
        return pred_boxes


class Box2BoxTransformRotated(object):
    """
    The box-to-box transform defined in Rotated R-CNN. The transformation is parameterized
    by 5 deltas: (dx, dy, dw, dh, da). The transformation scales the box's width and height
    by exp(dw), exp(dh), shifts a box's center by the offset (dx * width, dy * height),
    and rotate a box's angle by da (radians).
    Note: angles of deltas are in radians while angles of boxes are in degrees.
    """

    def __init__(self, weights, scale_clamp=_DEFAULT_SCALE_CLAMP):
        """
        Args:
            weights (5-element tuple): Scaling factors that are applied to the
                (dx, dy, dw, dh, da) deltas. These are treated as
                hyperparameters of the system.
            scale_clamp (float): When predicting deltas, the predicted box scaling
                factors (dw and dh) are clamped such that they are <= scale_clamp.
        """
        self.weights = weights
        self.scale_clamp = scale_clamp

    def get_deltas(self, src_boxes, target_boxes):
        """
        Get box regression transformation deltas (dx, dy, dw, dh, da) that can be used
        to transform the `src_boxes` into the `target_boxes`. That is, the relation
        ``target_boxes == self.apply_deltas(deltas, src_boxes)`` is true (unless
        any delta is too large and is clamped).

        Args:
            src_boxes (Tensor): Nx5 source boxes, e.g., object proposals
            target_boxes (Tensor): Nx5 target of the transformation, e.g., ground-truth
                boxes.
        """
        assert isinstance(src_boxes, torch.Tensor), type(src_boxes)
        assert isinstance(target_boxes, torch.Tensor), type(target_boxes)

        src_ctr_x, src_ctr_y, src_widths, src_heights, src_angles = torch.unbind(src_boxes, dim=1)

        target_ctr_x, target_ctr_y, target_widths, target_heights, target_angles = torch.unbind(target_boxes, dim=1)

        wx, wy, ww, wh, wa = self.weights
        dx = wx * (target_ctr_x - src_ctr_x) / src_widths
        dy = wy * (target_ctr_y - src_ctr_y) / src_heights
        dw = ww * torch.log(target_widths / src_widths)
        dh = wh * torch.log(target_heights / src_heights)
        # Angles of deltas are in radians while angles of boxes are in degrees.
        # the conversion to radians serve as a way to normalize the values
        da = target_angles - src_angles
        da = (da + 180.0) % 360.0 - 180.0  # make it in [-180, 180)
        da *= wa * math.pi / 180.0

        deltas = torch.stack((dx, dy, dw, dh, da), dim=1)
        assert (src_widths > 0).all().item(), "Input boxes to Box2BoxTransformRotated are not valid!"
        return deltas

    def apply_deltas(self, deltas, boxes):
        """
        Apply transformation `deltas` (dx, dy, dw, dh, da) to `boxes`.

        Args:
            deltas (Tensor): transformation deltas of shape (N, 5).
                deltas[i] represents box transformation for the single box boxes[i].
            boxes (Tensor): boxes to transform, of shape (N, 5)
        """
        assert deltas.shape[1] == 5 and boxes.shape[1] == 5
        assert torch.isfinite(deltas).all().item(), "Box regression deltas become infinite or NaN!"

        boxes = boxes.to(deltas.dtype)

        ctr_x = boxes[:, 0]
        ctr_y = boxes[:, 1]
        widths = boxes[:, 2]
        heights = boxes[:, 3]
        angles = boxes[:, 4]

        wx, wy, ww, wh, wa = self.weights

        dx = deltas[:, 0] / wx
        dy = deltas[:, 1] / wy
        dw = deltas[:, 2] / ww
        dh = deltas[:, 3] / wh
        da = deltas[:, 4] / wa

        # Prevent sending too large values into torch.exp()
        dw = torch.clamp(dw, max=self.scale_clamp)
        dh = torch.clamp(dh, max=self.scale_clamp)

        pred_boxes = torch.zeros_like(deltas)
        pred_boxes[..., 0] = dx * widths + ctr_x  # x_ctr
        pred_boxes[..., 1] = dy * heights + ctr_y  # y_ctr
        pred_boxes[..., 2] = torch.exp(dw) * widths  # width
        pred_boxes[..., 3] = torch.exp(dh) * heights  # height

        # Following original RRPN implementation,
        # angles of deltas are in radians while angles of boxes are in degrees.
        pred_angle = da * 180.0 / math.pi + angles
        pred_angle = (pred_angle + 180.0) % 360.0 - 180.0  # make it in [-180, 180)

        pred_boxes[..., 4] = pred_angle

        return pred_boxes


class Shift2BoxTransform(object):
    def __init__(self, weights):
        """
        Args:
            weights (4-element tuple): Scaling factors that are applied to the
                (dl, dt, dr, db) deltas.
        """
        self.weights = weights

    def get_deltas(self, shifts, boxes):
        """
        Get box regression transformation deltas (dl, dt, dr, db) that can be used
        to transform the `shifts` into the `boxes`. That is, the relation
        ``boxes == self.apply_deltas(deltas, shifts)`` is true.

        Args:
            shifts (Tensor): shifts, e.g., feature map coordinates
            boxes (Tensor): target of the transformation, e.g., ground-truth
                boxes.
        """
        assert isinstance(shifts, torch.Tensor), type(shifts)
        assert isinstance(boxes, torch.Tensor), type(boxes)

        deltas = torch.cat((shifts - boxes[..., :2], boxes[..., 2:] - shifts), dim=-1) * shifts.new_tensor(self.weights)
        return deltas

    def apply_deltas(self, deltas, shifts):
        """
        Apply transformation `deltas` (dl, dt, dr, db) to `shifts`.

        Args:
            deltas (Tensor): transformation deltas of shape (N, k*4), where k >= 1.
                deltas[i] represents k potentially different class-specific
                box transformations for the single shift shifts[i].
            shifts (Tensor): shifts to transform, of shape (N, 2)
        """
        assert torch.isfinite(deltas).all().item()
        shifts = shifts.to(deltas.dtype)

        if deltas.numel() == 0:
            return torch.empty_like(deltas)

        deltas = deltas.view(deltas.size()[:-1] + (-1, 4)) / shifts.new_tensor(self.weights)
        boxes = torch.cat(
            (shifts.unsqueeze(-2) - deltas[..., :2], shifts.unsqueeze(-2) + deltas[..., 2:]), dim=-1
        ).view(deltas.size()[:-2] + (-1,))
        return boxes
